Local features play a key role in many Computer Vision applications. Finding and matching them across images has been the subject of vast amounts of research. Until recently, the best techniques relied on carefully hand-crafted features as shown in references [1-5]. Over the past few years, as in many areas of Computer Vision, methods based in machine learning, and more specifically deep learning, have started to outperform these traditional methods as shown in references [6-10]. These new algorithms, however, address only a single step in the complete processing chain, which includes detecting the features, computing their orientation, and extracting robust representations that allow to match them across images. Therefore, in light of the deficiencies of the background art, novel and integrated solutions are desired for detecting features, computing their orientation, and extracting description of these features, in the field of image processing.